Linear Moving Horizon Estimation With Pre-Estimating Observer

Sui, Dan; Johansen, Tor Arne; Feng, Liuyang

doi:10.1109/tac.2010.2053060

Cited by 50 publications

(65 citation statements)

References 18 publications

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“…This greatly reduces the computational complexity of the approach. The use of pre-estimation distinguishes the approach in Sui et al (2010) and the present paper from the approaches known in the literature, e.g. Rao et al (2001Rao et al ( , 2003; Alessandri et al (2003Alessandri et al ( , 2004.…”

Section: Introductionmentioning

confidence: 98%

“…Recently, the authors proposed an improved linear MHE approach (Sui et al, 2010) with a pre-estimating linear estimator instead of open loop forward prediction equations normally being used in the cost functions and constraints of the MHE. This development was made in spirit to the so-called 'pre-stabilizing' model predictive control (Rossiter et al, 1998;Kouvaritakis et al, 2000), where the control sequence is parameterized as perturbations to a given pre-stabilizing feedback gain.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the method of Sui et al (2010) is extended to take into account state constraints, which can be expected to improve performance as in Rao et al (2001Rao et al ( , 2003; Haseltine and Rawlings (2005). Moreover, instead of using scalar weight parameters, weight matrices are introduced into the cost function.…”

Section: Introductionmentioning

confidence: 99%

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Linear constrained moving horizon estimator with pre-estimating observer

Sui

Johansen

2014

Systems & Control Letters

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In this paper, a constrained moving horizon estimation (MHE) strategy for linear systems is proposed. Recently, the use of a pre-estimating linear observer in the forward prediction equations in the MHE cost function has been proposed in order to reduce the effects of uncertainty. Here we introduce state constraints within this formulation, and investigate stability properties in the presence of bounded disturbances and noise. The robustness and performance of the proposed observer is demonstrated with a simulation example.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Linear constrained moving horizon estimator with pre-estimating observer

Sui

Johansen

2014

Systems & Control Letters

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“…Due to knowledge of the mapping H t (·), this uniquely defines the state estimates at the entire horizon, including the current state estimatê x t,t that is usually the main target of estimation. It is further remarked that the formulation can be extended with process noise (as in [2], [4]) or a Kalman-filter corrected predictor for pre-filtering of the a priori estimate (as in [37], [38]) in order to reduce the estimator's sensitivity to model errors and disturbances. For simplicity, we leave out this extension in the present paper.…”

Section: A Problem Formulationmentioning

confidence: 99%

Regularized Nonlinear Moving Horizon Observer for Detectable Systems

Sui

Johansen

2010

IFAC Proceedings Volumes

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Moving horizon estimation provides a general method for state estimation with strong theoretical convergence properties under the critical assumption that global solutions are found to the associated nonlinear programming problem at each sampling instant. A particular benefit of the approach is due to the use of a moving window of data that is used to update the estimate at each sampling instant. This provides robustness to temporary data deficiencies such as lack of excitation and measurement noise, and the inherent robustness can be further enhanced by introducing regularization mechanisms. In this paper we study moving horizon estimation in cases when output measurements are lost or delayed, which is a common situation when digitally coded data are received over low quality communication channels or random access networks. Modifications to a basic moving horizon state estimation algorithm and conditions for exponential convergence of the estimation errors are given, and the method is illustrated by using a simulation example and experimental data from an offshore oil drilling operation.

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“…Because MHE avoids the computational burden of a full information estimator by considering only a window of data, stability issue on the performance of MHE arises [33]. Stability properties of MHE for constrained linear and nonlinear systems have been investigated in [26]- [29], [34]- [36]. In [27]- [29], the existence of bounded sequence on the estimation error was proved.…”

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confidence: 99%